An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the Dynamic decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. The results of the currents and voltages norms are applied as features for a topology data analysis algorithm for fault type classifying in the AC microgrid for fault location purposes. The Algorithm was tested on a microgrid that operates with precision equal to 100% in fault classification and a mean error lower than 20 m when forecasting the fault location. The proposed method robustly operates in sampling frequency, fault resistance variation, and noisy and high impedance fault conditions.

Since the most important issue in the production of digital currencies is energy consumption, the ‎use of illegal electricity in mining farms has become very popular. Illegal mining is particularly ‎important in countries such as Iran where the price of electrical energy is extremely low. This issue ‎has caused numerous problems such as frequent blackouts, large losses for industries and even ‎daily power cuts in several large cities. Previous machine learning approaches for miner detection ‎are mostly supervised methods which rely on labeled data. Due to the fact that the number of ‎labeled data is very limited in reality, we propose unsupervised methods in this paper. A real data ‎set from Markazi Province Distribution Company in Iran has been employed to produce the results. ‎The classification process consists of two stages: in the first stage, Dynamic mode decomposition ‎‎(DMD) has been used to extract new features which compose the set of features along with certain ‎factors from the Advanced Metering Infrastructure (AMI). These features are selected for 58 ‎subscribers with positive and negative labels. In the second stage, a number of unsupervised models ‎are built from the results of the first stage. The highest accuracy of classification obtained is 74% ‎from unsupervised algorithms and 85% for supervised algorithms, which is very significant ‎considering the fact that unsupervised algorithms do not need labeled data‎.‎

‎Forecasting price trends in financial markets is of particular importance for traders because price trends are inherently Dynamic and forecasting these trends is complicated‎. In this study‎, ‎we present a new hybrid method based on combination of the Dynamic mode decomposition method and long short-term memory method for forecasting financial markets‎. This new method is in this way that we first extract the dominant and coherent data using the Dynamic mode decomposition method and then predict financial market trends with the help of these data and the long short-term memory method‎.‎ To demonstrate the efficacy of this method‎, ‎we present three practical examples‎: ‎closing price of US Dollar to Iranian Rial‎, ‎closing prices of zob roy Isfahan stock‎, ‎and also closing prices of siman shargh stock‎. ‎These examples exhibit bullish‎, ‎bearish‎, ‎and neutral behaviors‎, ‎respectively‎.‎ It seems that the proposed new method works better in predicting the financial market than the existing long-short-term memory method‎.

Background: Electrocardiogram (ECG) plays a vital role in the analysis of heart activity. It can be used to analyze the different heart diseases and mental stress assessment also. Various noises, such as baseline wandering, muscle artifacts and power line interface disturbs the information within the ECG signal. To acquire correct information from ECG signal, these noises should be removed. Methods: In the proposed work, the improved variational mode decomposition (IVMD) method for the removal of noise in ECG signals is used. In the proposed method, the weighted signal amplitude integrated over the timeframe of the ECG signal varies the window size during decomposition. Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method. Results: The performance comparison of traditional variational mode decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. The extracted highest signal to noise ratio (SNR) value of acquired ECG signals using traditional VMD is 42db whereas highest value of signal to noise ratio (SNR) using improved VMD (IVMD) is 83db. Conclusion: The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.